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Abstract

It is argued that models are conceptualized, designed, developed, and validated to understand complex behaviour of larger entities. Models provide indicative measurements, and their validations in real life situation need careful considerations of relevant ambient conditions. Models also provide suggestive and causal relationships among their qualitative and quantitative influencers for better predictability. Generally, predictive models provide structural equations, measurement equations with associated random errors. These errors do play vital roles in relating abstracted behavior of the model outputs with the real life situations. In order to reduce these errors to an agreed level, case-based validations of models are quite important. This chapter discusses derived measurement and structural equations that the model has produced and presents some cases to examine the appropriateness of the application of the model developed.

Derived Measurement Equations Of The Model

Measurement equations show the relationships among latent and exogenous variables. “Classical test theory” is the basis for establishing this relationship (Cronbach et al., 1972; Linn and Werts, 1979; Susan et al., 2008; España et al., 2010). The general form of the measurement equation is shown in Figure 1.

Figure 1.

Measurement equation (SEM) (España et al., 2010)

The measurement equation here is X = + where X is the exogenous variables which is the mean of the items summated through dummy coding as explained in chapters eight and nine. represents the endogenous variable in the equation. Each variable X thus represents a set of independent indicators (questions) administered to the respondents and ordinal data are acquired. Classical test theory considers measurement errors () and provides a predictive ability to the relationship between exogenous variable and predictors. Table 1 describes the relationships and displays the foundation for such predictions.